scholarly journals A semiparametric linear transformation model to estimate causal effects for survival data

2013 ◽  
Vol 42 (1) ◽  
pp. 18-35 ◽  
Author(s):  
Huazhen Lin ◽  
Yi Li ◽  
Liang Jiang ◽  
Gang Li
Keyword(s):  
Linear Transformation ◽  
Survival Data ◽  
Transformation Model ◽  
Causal Effects ◽  
Linear Transformation Model

SEMIPARAMETRIC ESTIMATION OF PARTIALLY LINEAR TRANSFORMATION MODELS UNDER CONDITIONAL QUANTILE RESTRICTION

2014 ◽  
Vol 32 (2) ◽  
pp. 458-497 ◽  
Author(s):  
Zhengyu Zhang
Keyword(s):  
Linear Transformation ◽  
Semiparametric Estimation ◽  
Transformation Model ◽  
Dimensional Parameter ◽  
Conditional Quantile ◽  
Infinite Dimensional ◽  
Linear Transformation Model ◽  
Partially Linear ◽  
Linear Transformation Models ◽  
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This article is concerned with semiparametric estimation of a partially linear transformation model under conditional quantile restriction with no parametric restriction imposed either on the link functional form or on the error term distribution. We describe for the finite-dimensional parameter a$\sqrt n$-consistent estimator which combines the features of Chen (2010)’s maximum integrated score estimator as well as Lee (2003)’s average quantile regression. We show the remaining two infinite-dimensional unknown functions in the model can be separately identified and propose estimators for these functions based on the marginal integration method. Furthermore, a simple approach is proposed to estimate the average partial quantile effect. Two important extensions, i.e., random censoring as well as estimating a transformation model with an endogenous regressor are also considered.

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